Computational modelling of patient specific spring assisted lambdoid craniosynostosis correction

Selim Bozkurt, Alessandro Borghi, Lara S. van de Lande, N. U.Owase Jeelani, David J. Dunaway, Silvia Schievano

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
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Abstract

Lambdoid craniosynostosis (LC) is a rare non-syndromic craniosynostosis characterised by fusion of the lambdoid sutures at the back of the head. Surgical correction including the spring assisted cranioplasty is the only option to correct the asymmetry at the skull in LC. However, the aesthetic outcome from spring assisted cranioplasty may remain suboptimal. The aim of this study is to develop a parametric finite element (FE) model of the LC skulls that could be used in the future to optimise spring surgery. The skull geometries from three different LC patients who underwent spring correction were reconstructed from the pre-operative computed tomography (CT) in Simpleware ScanIP. Initially, the skull growth between the pre-operative CT imaging and surgical intervention was simulated using MSC Marc. The osteotomies and spring implantation were performed to simulate the skull expansion due to the spring forces and skull growth between surgery and post-operative CT imaging in MSC Marc. Surface deviation between the FE models and post-operative skull models reconstructed from CT images changed between ± 5 mm over the skull geometries. Replicating spring assisted cranioplasty in LC patients allow to tune the parameters for surgical planning, which may help to improve outcomes in LC surgeries in the future.

Original languageEnglish
Article number18693
JournalScientific Reports
Volume10
Issue number1
Early online date29 Oct 2020
DOIs
Publication statusPublished - 1 Dec 2020

Bibliographical note

Funding Information:
The work has been funded by Great Ormond Street Hospital for Children Charity (Grant number 12SG15), the NIHR GOSH/UCL Biomedical Research Centre Advanced Therapies for Structural Malformations and Tissue Damage pump-prime funding call (Grant number 17DS18), the Engineering and Physical Sciences Research Council (EP/N02124X/1) and the European Research Council (ERC-2017-StG-757923). This report incorporates independent research from the National Institute for Health Research Biomedical Research Centre Funding Scheme. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.

Publisher Copyright:
© 2020, The Author(s).

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